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    <abstract>In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.</abstract>
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%0 Conference Proceedings
%T Automatic Difficulty Classification of Arabic Sentences
%A Khallaf, Nouran
%A Sharoff, Serge
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F khallaf-sharoff-2021-automatic
%X In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.
%U https://aclanthology.org/2021.wanlp-1.11
%P 105-114

Markdown (Informal)

[Automatic Difficulty Classification of Arabic Sentences](https://aclanthology.org/2021.wanlp-1.11) (Khallaf & Sharoff, WANLP 2021)

• Automatic Difficulty Classification of Arabic Sentences (Khallaf & Sharoff, WANLP 2021)

ACL

• Nouran Khallaf and Serge Sharoff. 2021. Automatic Difficulty Classification of Arabic Sentences. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 105–114, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.